causality

Caltech researchers are applying mathematical models to gain a clearer understanding of cause-and-effect relationships across fields like AI, biology, and economics. By advancing causal inference, they can distinguish between mere correlation and genuine causation. This approach enhances the accuracy of predictions and decision-making, allowing more refined insights into complex systems. The project’s applications are broad, from medical research to economic forecasting, ultimately aiming to reveal true causes behind observed effects and support human decision-making with greater precision.